{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T19:11:26.205320Z",
     "start_time": "2019-05-03T19:11:26.202770Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 75401,
     "status": "error",
     "timestamp": 1556727122761,
     "user": {
      "displayName": "Alan Lau",
      "photoUrl": "https://lh3.googleusercontent.com/-ZaOsrA7oyQw/AAAAAAAAAAI/AAAAAAAAAAc/PW0qJYFKbUY/s64/photo.jpg",
      "userId": "02808478473948384287"
     },
     "user_tz": -480
    },
    "id": "H4_rgcWjC1wQ",
    "outputId": "4f67fd54-1f4d-4f3d-c880-0db8b5ab9e3e"
   },
   "outputs": [],
   "source": [
    "# import getpass\n",
    "# from oauth2client.client import GoogleCredentials\n",
    "# from google.colab import auth\n",
    "# !apt-get install - y - qq software-properties-common python-software-properties module-init-tools\n",
    "# !add-apt-repository - y ppa: alessandro-strada/ppa 2 > &1 > /dev/null\n",
    "# !apt-get update - qq 2 > &1 > /dev/null\n",
    "# !apt-get - y install - qq google-drive-ocamlfuse fuse\n",
    "# auth.authenticate_user()\n",
    "# creds = GoogleCredentials.get_application_default()\n",
    "# !google-drive-ocamlfuse - headless - id = {creds.client_id} - secret = {creds.client_secret} < /dev/null 2 > &1 | grep URL\n",
    "# vcode = getpass.getpass()\n",
    "# !echo {vcode} | google-drive-ocamlfuse - headless - id = {creds.client_id} - secret = {creds.client_secret}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T19:11:26.210992Z",
     "start_time": "2019-05-03T19:11:26.208365Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "sAEXzMr_DOTo"
   },
   "outputs": [],
   "source": [
    "# !mkdir - p drive\n",
    "# !google-drive-ocamlfuse drive - o nonempty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T19:11:26.215698Z",
     "start_time": "2019-05-03T19:11:26.213315Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "MCDdayZDDVVs"
   },
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.chdir('drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T19:11:26.221614Z",
     "start_time": "2019-05-03T19:11:26.218969Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3270,
     "status": "ok",
     "timestamp": 1556727041091,
     "user": {
      "displayName": "Alan Lau",
      "photoUrl": "https://lh3.googleusercontent.com/-ZaOsrA7oyQw/AAAAAAAAAAI/AAAAAAAAAAc/PW0qJYFKbUY/s64/photo.jpg",
      "userId": "02808478473948384287"
     },
     "user_tz": -480
    },
    "id": "6AH8_4oADX8R",
    "outputId": "de369089-fcfc-4d3d-8fbd-17517f9d4a92"
   },
   "outputs": [],
   "source": [
    "# !ls Competitions/KDD-Cup-2019-CAMMTR/Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:24:56.288707Z",
     "start_time": "2019-05-11T07:24:54.792279Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "OxlyOW7bCvXc"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/lightgbm/__init__.py:46: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.3) compiler.\n",
      "This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.\n",
      "Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.\n",
      "You can install the OpenMP library by the following command: ``brew install libomp``.\n",
      "  \"You can install the OpenMP library by the following command: ``brew install libomp``.\", UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import ast\n",
    "from itertools import product\n",
    "\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import json\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import lightgbm as lgb\n",
    "import os\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# project_path='./Competitions/KDD-Cup-2019-CAMMTR/'\n",
    "project_path = '../../'\n",
    "mainpath = project_path + r'Data/data_set_phase1/'\n",
    "\n",
    "\n",
    "holidays = pd.Series(\n",
    "    json.load(open(project_path+r'Config/Holidays.json'))['holidays'])\n",
    "subwayinfo = pd.read_csv(project_path+r'Data/Beijing.csv')\n",
    "weatherinfo = pd.read_csv(project_path+r'Data/Weather.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AJFW6SL_CvXf"
   },
   "source": [
    "# Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:24:58.952206Z",
     "start_time": "2019-05-11T07:24:58.947593Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "kCAkXW4fCvXg"
   },
   "outputs": [],
   "source": [
    "def load_data(mainpath):\n",
    "    train_queries = pd.read_csv(\n",
    "        mainpath + 'train_queries.csv', parse_dates=['req_time'])\n",
    "    train_plans = pd.read_csv(mainpath + 'train_plans.csv',\n",
    "                              parse_dates=['plan_time'])\n",
    "    train_clicks = pd.read_csv(mainpath + 'train_clicks.csv')\n",
    "#     profiles = pd.read_csv(mainpath + 'profiles.csv')\n",
    "    test_queries = pd.read_csv(\n",
    "        mainpath + 'test_queries.csv', parse_dates=['req_time'])\n",
    "    test_plans = pd.read_csv(mainpath + 'test_plans.csv',\n",
    "                             parse_dates=['plan_time'])\n",
    "    return train_queries, train_plans, train_clicks, test_queries, test_plans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:24:59.588063Z",
     "start_time": "2019-05-11T07:24:59.584008Z"
    }
   },
   "outputs": [],
   "source": [
    "def read_profile_data():\n",
    "    profile_data = pd.read_csv(mainpath + 'profiles.csv')\n",
    "    profile_na = np.zeros(67)\n",
    "    profile_na[0] = -1\n",
    "    profile_na = pd.DataFrame(profile_na.reshape(1, -1))\n",
    "    profile_na.columns = profile_data.columns\n",
    "    profile_data = profile_data.append(profile_na)\n",
    "    return profile_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:25:00.307881Z",
     "start_time": "2019-05-11T07:25:00.303054Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "iUQcoS3ACvXi"
   },
   "outputs": [],
   "source": [
    "def merge(train_queries, train_plans, train_clicks, test_queries, test_plans):\n",
    "    train = train_queries.merge(train_plans, 'left', ['sid'])\n",
    "    test = test_queries.merge(test_plans, 'left', ['sid'])\n",
    "    train = train.merge(train_clicks, 'left', ['sid'])\n",
    "    train['click_mode'] = train['click_mode'].fillna(0).astype(int)\n",
    "    data = pd.concat([train, test], ignore_index=True)\n",
    "#     data = data.merge(profiles, 'left', ['pid'])\n",
    "    return train, test, data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:25:02.434945Z",
     "start_time": "2019-05-11T07:25:02.408022Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ezvgAZ46CvXj"
   },
   "outputs": [],
   "source": [
    "def gen_plan_feas(data):\n",
    "    n = data.shape[0]\n",
    "    mode_list_feas = np.zeros((n, 12))\n",
    "\n",
    "    each_mode_feas = np.zeros((n, 22))\n",
    "    speed, pricePerDis = np.zeros((n, 11)), np.zeros((n, 11))\n",
    "\n",
    "    max_dist, min_dist, mean_dist, std_dist = np.zeros(\n",
    "        (n,)), np.zeros((n,)), np.zeros((n,)), np.zeros((n,))\n",
    "\n",
    "    max_price, min_price, mean_price, std_price = np.zeros(\n",
    "        (n,)), np.zeros((n,)), np.zeros((n,)), np.zeros((n,))\n",
    "\n",
    "    max_eta, min_eta, mean_eta, std_eta = np.zeros(\n",
    "        (n,)), np.zeros((n,)), np.zeros((n,)), np.zeros((n,))\n",
    "\n",
    "    min_dist_mode, max_dist_mode, min_price_mode, max_price_mode, min_eta_mode, max_eta_mode, first_mode = \\\n",
    "        np.zeros((n,)), np.zeros((n,)), np.zeros((n,)), np.zeros(\n",
    "            (n,)), np.zeros((n,)), np.zeros((n,)), np.zeros((n,))\n",
    "\n",
    "    mode_texts = []\n",
    "    for i, plan in tqdm(enumerate(data['plans_json'].values)):\n",
    "        if len(plan) == 0:\n",
    "            cur_plan_list = []\n",
    "        else:\n",
    "            cur_plan_list = plan\n",
    "        if len(cur_plan_list) == 0:\n",
    "            mode_list_feas[i, 0] = 1\n",
    "            first_mode[i] = 0\n",
    "            max_dist[i] = -1\n",
    "            min_dist[i] = -1\n",
    "            mean_dist[i] = -1\n",
    "            std_dist[i] = -1\n",
    "            max_price[i] = -1\n",
    "            min_price[i] = -1\n",
    "            mean_price[i] = -1\n",
    "            std_price[i] = -1\n",
    "            max_eta[i] = -1\n",
    "            min_eta[i] = -1\n",
    "            mean_eta[i] = -1\n",
    "            std_eta[i] = -1\n",
    "            min_dist_mode[i] = -1\n",
    "            max_dist_mode[i] = -1\n",
    "            min_price_mode[i] = -1\n",
    "            max_price_mode[i] = -1\n",
    "            min_eta_mode[i] = -1\n",
    "            max_eta_mode[i] = -1\n",
    "            mode_texts.append('word_null')\n",
    "\n",
    "            speed[i] = -1\n",
    "            pricePerDis[i] = -1\n",
    "        else:\n",
    "            distance_list = []\n",
    "            price_list = []\n",
    "            eta_list = []\n",
    "            mode_list = []\n",
    "            for tmp_dit in cur_plan_list:\n",
    "                distance_list.append(int(tmp_dit['distance']))\n",
    "                if tmp_dit['price'] == '':\n",
    "                    price_list.append(0)\n",
    "                else:\n",
    "                    price_list.append(int(tmp_dit['price']))\n",
    "                eta_list.append(int(tmp_dit['eta']))\n",
    "                mode_list.append(int(tmp_dit['transport_mode']))\n",
    "            mode_texts.append(\n",
    "                ' '.join(['word_{}'.format(mode) for mode in mode_list]))\n",
    "            distance_list = np.array(distance_list)\n",
    "            price_list = np.array(price_list)\n",
    "            eta_list = np.array(eta_list)\n",
    "            mode_list = np.array(mode_list, dtype='int')\n",
    "            mode_list_feas[i, mode_list] = 1\n",
    "            distance_sort_idx = np.argsort(distance_list)\n",
    "            price_sort_idx = np.argsort(price_list)\n",
    "            eta_sort_idx = np.argsort(eta_list)\n",
    "            max_dist[i] = distance_list[distance_sort_idx[-1]]\n",
    "            min_dist[i] = distance_list[distance_sort_idx[0]]\n",
    "            mean_dist[i] = np.mean(distance_list)\n",
    "            std_dist[i] = np.std(distance_list)\n",
    "            max_price[i] = price_list[price_sort_idx[-1]]\n",
    "            min_price[i] = price_list[price_sort_idx[0]]\n",
    "            mean_price[i] = np.mean(price_list)\n",
    "            std_price[i] = np.std(price_list)\n",
    "            max_eta[i] = eta_list[eta_sort_idx[-1]]\n",
    "            min_eta[i] = eta_list[eta_sort_idx[0]]\n",
    "            mean_eta[i] = np.mean(eta_list)\n",
    "            std_eta[i] = np.std(eta_list)\n",
    "            first_mode[i] = mode_list[0]\n",
    "            max_dist_mode[i] = mode_list[distance_sort_idx[-1]]\n",
    "            min_dist_mode[i] = mode_list[distance_sort_idx[0]]\n",
    "            max_price_mode[i] = mode_list[price_sort_idx[-1]]\n",
    "            min_price_mode[i] = mode_list[price_sort_idx[0]]\n",
    "            max_eta_mode[i] = mode_list[eta_sort_idx[-1]]\n",
    "            min_eta_mode[i] = mode_list[eta_sort_idx[0]]\n",
    "\n",
    "            _speed = round(int(tmp_dit['distance'])/int(tmp_dit['eta']), 4)\n",
    "            if tmp_dit['price'] == '':\n",
    "                tmp_dit['price'] = 0\n",
    "            _pricePerDis = round(\n",
    "                int(tmp_dit['price'])/int(tmp_dit['distance']), 4)\n",
    "            speed[i][int(tmp_dit['transport_mode'])-1] = _speed\n",
    "            pricePerDis[i][int(tmp_dit['transport_mode'])-1] = _pricePerDis\n",
    "\n",
    "    feature_data = pd.DataFrame(mode_list_feas)\n",
    "    feature_data.columns = ['mode_feas_{}'.format(i) for i in range(12)]\n",
    "    feature_data['max_dist'] = max_dist\n",
    "    feature_data['min_dist'] = min_dist\n",
    "    feature_data['mean_dist'] = mean_dist\n",
    "    feature_data['std_dist'] = std_dist\n",
    "    feature_data['max_price'] = max_price\n",
    "    feature_data['min_price'] = min_price\n",
    "    feature_data['mean_price'] = mean_price\n",
    "    feature_data['std_price'] = std_price\n",
    "    feature_data['max_eta'] = max_eta\n",
    "    feature_data['min_eta'] = min_eta\n",
    "    feature_data['mean_eta'] = mean_eta\n",
    "    feature_data['std_eta'] = std_eta\n",
    "    feature_data['max_dist_mode'] = max_dist_mode\n",
    "    feature_data['min_dist_mode'] = min_dist_mode\n",
    "    feature_data['max_price_mode'] = max_price_mode\n",
    "    feature_data['min_price_mode'] = min_price_mode\n",
    "    feature_data['max_eta_mode'] = max_eta_mode\n",
    "    feature_data['min_eta_mode'] = min_eta_mode\n",
    "    feature_data['first_mode'] = first_mode\n",
    "\n",
    "    print('mode tfidf...')\n",
    "    tfidf_enc = TfidfVectorizer(ngram_range=(1, 2))\n",
    "    tfidf_vec = tfidf_enc.fit_transform(mode_texts)\n",
    "    svd_enc = TruncatedSVD(n_components=10, n_iter=20, random_state=2019)\n",
    "    mode_svd = svd_enc.fit_transform(tfidf_vec)\n",
    "    mode_svd = pd.DataFrame(mode_svd)\n",
    "    mode_svd.columns = ['svd_mode_{}'.format(i) for i in range(10)]\n",
    "\n",
    "    eachmode_data = pd.DataFrame(each_mode_feas)\n",
    "    eachmode_data.columns = ['fea_{}'.format(i) for i in range(22)]\n",
    "\n",
    "    for i in range(22):\n",
    "        if i % 2 == 0:\n",
    "            eachmode_data['fea_{}'.format(i)] = speed[:, int(i/2)]\n",
    "        else:\n",
    "            eachmode_data['fea_{}'.format(i)] = pricePerDis[:, int(i/2)]\n",
    "\n",
    "    plan_fea = pd.concat([feature_data, mode_svd, eachmode_data], axis=1)\n",
    "    plan_fea['sid'] = data['sid'].values\n",
    "\n",
    "    return plan_fea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:25:03.261326Z",
     "start_time": "2019-05-11T07:25:03.255670Z"
    }
   },
   "outputs": [],
   "source": [
    "def gen_profile_feas(data):\n",
    "    profile_data = read_profile_data()\n",
    "    x = profile_data.drop(['pid'], axis=1).values\n",
    "    svd = TruncatedSVD(n_components=33, n_iter=20, random_state=2019)\n",
    "    svd_x = svd.fit_transform(x)\n",
    "    svd_feas = pd.DataFrame(svd_x)\n",
    "    svd_feas.columns = ['svd_fea_{}'.format(i) for i in range(33)]\n",
    "    svd_feas['pid'] = profile_data['pid'].values\n",
    "    data['pid'] = data['pid'].fillna(-1)\n",
    "    data = data.merge(svd_feas, on='pid', how='left')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T04:22:32.301017Z",
     "start_time": "2019-05-03T04:22:32.296715Z"
    }
   },
   "source": [
    "# Add Centroid distance & Station distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:25:11.000229Z",
     "start_time": "2019-05-11T07:25:10.990087Z"
    }
   },
   "outputs": [],
   "source": [
    "def trans_features(data):\n",
    "    o_co = data[['o']]\n",
    "    d_co = data[['d']]\n",
    "\n",
    "    o_co.columns = ['co']\n",
    "    d_co.columns = ['co']\n",
    "\n",
    "    all_co = pd.concat([d_co, o_co]).drop_duplicates()\n",
    "\n",
    "    all_co['lng'] = all_co['co'].apply(lambda x: float(x.split(',')[0]))\n",
    "    all_co['lat'] = all_co['co'].apply(lambda x: float(x.split(',')[1]))\n",
    "\n",
    "    lng_mean = all_co['lng'].mean()\n",
    "    lat_mean = all_co['lat'].mean()\n",
    "\n",
    "    lng_mode = all_co['lng'].mode()[0]\n",
    "    lat_mode = all_co['lat'].mode()[0]\n",
    "\n",
    "    data['o_main_centroid_mean_dis'] = abs(\n",
    "        data['o_lng']-lng_mean)+abs(data['o_lat']-lat_mean)\n",
    "    data['d_main_centroid_mean_dis'] = abs(\n",
    "        data['d_lng']-lng_mean)+abs(data['d_lat']-lat_mean)\n",
    "\n",
    "    data['o_main_centroid_mode_dis'] = abs(\n",
    "        data['o_lng']-lng_mean)+abs(data['o_lat']-lat_mean)\n",
    "    data['d_main_centroid_mode_dis'] = abs(\n",
    "        data['d_lng']-lng_mode)+abs(data['d_lat']-lat_mode)\n",
    "\n",
    "\n",
    "    data['o_nearest_dis'] = np.nan\n",
    "    data['d_nearest_dis'] = np.nan\n",
    "\n",
    "\n",
    "    all_co = pd.concat([d_co, o_co])['co'].unique()\n",
    "\n",
    "    for co in tqdm(all_co):\n",
    "        lg, la = co.split(',')\n",
    "        min_dis = (abs(subwayinfo['station_longitude']-float(lg)) +\n",
    "                   abs(subwayinfo['station_latitude']-float(la))).min()\n",
    "        data.loc[(data['o'] == co), 'o_nearest_dis'] = min_dis\n",
    "        data.loc[(data['d'] == co), 'd_nearest_dis'] = min_dis\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weather features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:25:12.452853Z",
     "start_time": "2019-05-11T07:25:12.448180Z"
    }
   },
   "outputs": [],
   "source": [
    "def weather_feature(data):\n",
    "    weatherinfo.rename(columns={'date': 'req_date'}, inplace=True)\n",
    "    weatherinfo['weather'] = (weatherinfo['weather'].map(\n",
    "        {'q': 0, 'dy': 1, 'dyq': 2, 'qdy': 3, 'xq': 4, 'xydy': 5})).astype(int)\n",
    "    data = data.merge(weatherinfo, on='req_date', how='left')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:25:13.724899Z",
     "start_time": "2019-05-11T07:25:13.713940Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "1QIdrjCiCvXw"
   },
   "outputs": [],
   "source": [
    "def feature_engineering(df):\n",
    "    print('generate profile features...')\n",
    "    df = gen_profile_feas(df)\n",
    "    \n",
    "    print('generate time features...')\n",
    "    \n",
    "    df['time_diff'] = df['plan_time'].astype(\n",
    "        int) - df['req_time'].astype(int)\n",
    "\n",
    "    df['plans_json'] = data['plans'].fillna(\n",
    "        '[]').apply(lambda x: json.loads(x))\n",
    "    df_plans = gen_plan_feas(df)\n",
    "    plan_features = [col for col in df_plans.columns if col not in ['sid']]\n",
    "    df = df.merge(df_plans, on='sid', how='left')\n",
    "\n",
    "    df['req_time'] = pd.to_datetime(df['req_time'])\n",
    "    df['day_of_week'] = data['req_time'].dt.dayofweek\n",
    "    df['req_date'] = df['req_time'].dt.strftime('%m-%d')\n",
    "    df['req_hour'] = df['req_time'].dt.hour\n",
    "    df['req_minute'] = df['req_time'].dt.minute\n",
    "    df['if_holiday'] = (df['req_date'].isin(holidays)).astype(int)\n",
    "\n",
    "#     # Week day\n",
    "#     current_c = list(df.columns.values)\n",
    "#     weekdays = ['Monday', 'Tuesday', 'Wednesday',\n",
    "#                 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "#     current_c.extend(weekdays)\n",
    "#     df = df.reindex(columns=current_c, fill_value=0)\n",
    "#     for day_name in weekdays:\n",
    "#         df.loc[(df['day_of_week'] == day_name), [day_name]] = 1\n",
    "#     print('week day Done.')\n",
    "\n",
    "#     # Hour\n",
    "#     current_c = list(df.columns.values)\n",
    "#     oclock = [str(i)+'_oclock' for i in range(0, 24)]\n",
    "#     current_c.extend(oclock)\n",
    "#     df = df.reindex(columns=current_c, fill_value=0)\n",
    "#     for h in oclock:\n",
    "#         df.loc[(df['req_hour'] == int(h.split('_')[0])), [h]] = 1\n",
    "\n",
    "#     print('Hour Done.')\n",
    "\n",
    "\n",
    "#     df.drop(columns=['day_of_week', 'req_hour'], inplace=True)\n",
    "    print('generate distance features...')\n",
    "\n",
    "    df['o_lng'] = df['o'].apply(lambda x: float(x.split(',')[0]))\n",
    "    df['o_lat'] = df['o'].apply(lambda x: float(x.split(',')[1]))\n",
    "    df['d_lng'] = df['d'].apply(lambda x: float(x.split(',')[0]))\n",
    "    df['d_lat'] = df['d'].apply(lambda x: float(x.split(',')[1]))\n",
    "    df['od_manhattan_distance'] = abs(\n",
    "        df['o_lng']-df['d_lng'])+abs(df['o_lat']-df['d_lat'])\n",
    "\n",
    "    print('OD distance done.')\n",
    "\n",
    "    df = trans_features(df)\n",
    "    \n",
    "    print('generate weather features...')\n",
    "    df = weather_feature(df)\n",
    "\n",
    "    return df, plan_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:25:18.477712Z",
     "start_time": "2019-05-11T07:25:14.681963Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "HxBaCBRxCvXo"
   },
   "outputs": [],
   "source": [
    "train_queries, train_plans, train_clicks, test_queries, test_plans = load_data(\n",
    "    mainpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:25:19.503632Z",
     "start_time": "2019-05-11T07:25:18.588168Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "wfxInGIJCvXq"
   },
   "outputs": [],
   "source": [
    "train, test, data = merge(train_queries, train_plans,\n",
    "                          train_clicks, test_queries, test_plans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:34:10.105100Z",
     "start_time": "2019-05-11T07:25:22.726594Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2393,
     "status": "error",
     "timestamp": 1556727019156,
     "user": {
      "displayName": "Alan Lau",
      "photoUrl": "https://lh3.googleusercontent.com/-ZaOsrA7oyQw/AAAAAAAAAAI/AAAAAAAAAAc/PW0qJYFKbUY/s64/photo.jpg",
      "userId": "02808478473948384287"
     },
     "user_tz": -480
    },
    "id": "BplV1S9vCvXy",
    "outputId": "064ba387-36cc-4067-b32b-de7f2c86fa75"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generate profile features...\n",
      "generate time features...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "594358it [01:32, 6407.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode tfidf...\n",
      "generate distance features...\n",
      "OD distance done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6525/6525 [06:39<00:00, 16.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generate weather features...\n"
     ]
    }
   ],
   "source": [
    "data, plan_features = feature_engineering(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T07:16:23.279831Z",
     "start_time": "2019-05-11T07:16:23.273293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['click_mode', 'click_time', 'd', 'o', 'pid', 'plan_time', 'plans',\n",
       "       'req_time', 'sid', 'svd_fea_0', 'svd_fea_1', 'svd_fea_2',\n",
       "       'svd_fea_3', 'svd_fea_4', 'svd_fea_5', 'svd_fea_6', 'svd_fea_7',\n",
       "       'svd_fea_8', 'svd_fea_9', 'svd_fea_10', 'svd_fea_11', 'svd_fea_12',\n",
       "       'svd_fea_13', 'svd_fea_14', 'svd_fea_15', 'svd_fea_16',\n",
       "       'svd_fea_17', 'svd_fea_18', 'svd_fea_19', 'time_diff',\n",
       "       'plans_json', 'mode_feas_0', 'mode_feas_1', 'mode_feas_2',\n",
       "       'mode_feas_3', 'mode_feas_4', 'mode_feas_5', 'mode_feas_6',\n",
       "       'mode_feas_7', 'mode_feas_8', 'mode_feas_9', 'mode_feas_10',\n",
       "       'mode_feas_11', 'max_dist', 'min_dist', 'mean_dist', 'std_dist',\n",
       "       'max_price', 'min_price', 'mean_price', 'std_price', 'max_eta',\n",
       "       'min_eta', 'mean_eta', 'std_eta', 'max_dist_mode', 'min_dist_mode',\n",
       "       'max_price_mode', 'min_price_mode', 'max_eta_mode', 'min_eta_mode',\n",
       "       'first_mode', 'svd_mode_0', 'svd_mode_1', 'svd_mode_2',\n",
       "       'svd_mode_3', 'svd_mode_4', 'svd_mode_5', 'svd_mode_6',\n",
       "       'svd_mode_7', 'svd_mode_8', 'svd_mode_9', 'fea_0', 'fea_1',\n",
       "       'fea_2', 'fea_3', 'fea_4', 'fea_5', 'fea_6', 'fea_7', 'fea_8',\n",
       "       'fea_9', 'fea_10', 'fea_11', 'fea_12', 'fea_13', 'fea_14',\n",
       "       'fea_15', 'fea_16', 'fea_17', 'fea_18', 'fea_19', 'fea_20',\n",
       "       'fea_21', 'day_of_week', 'req_date', 'req_hour', 'req_minute',\n",
       "       'if_holiday', 'o_lng', 'o_lat', 'd_lng', 'd_lat',\n",
       "       'od_manhattan_distance'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reduce memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-06T15:50:22.650832Z",
     "start_time": "2019-05-06T15:50:22.641810Z"
    }
   },
   "outputs": [],
   "source": [
    "def mem_usage(pandas_obj):\n",
    "    if isinstance(pandas_obj, pd.DataFrame):\n",
    "        usage_b = pandas_obj.memory_usage(deep=True).sum()\n",
    "    else:  # 我们假设这不是一个df，而是一个 Series\n",
    "        usage_b = pandas_obj.memory_usage(deep=True)\n",
    "    usage_mb = usage_b / 1024 ** 2  # 将 bytes 转化成 megabytes\n",
    "    return \"{:03.2f} MB\".format(usage_mb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-06T15:50:40.088828Z",
     "start_time": "2019-05-06T15:50:24.578264Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "312.89 MB\n",
      "158.71 MB\n",
      "795.19 MB\n",
      "641.01 MB\n"
     ]
    }
   ],
   "source": [
    "data_float = data.select_dtypes(include=['float'])\n",
    "converted_float = data_float.apply(pd.to_numeric, downcast='float')\n",
    "\n",
    "print(mem_usage(data_float))\n",
    "print(mem_usage(converted_float))\n",
    "\n",
    "optimized_data = data.copy()\n",
    "\n",
    "optimized_data[converted_float.columns] = converted_float\n",
    "\n",
    "print(mem_usage(data))\n",
    "print(mem_usage(optimized_data))\n",
    "data = optimized_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T09:35:47.834101Z",
     "start_time": "2019-05-11T09:35:47.827372Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "rsLlF6-UCvXz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 122 features\n"
     ]
    }
   ],
   "source": [
    "# profile_feature = ['p' + str(i) for i in range(66)]\n",
    "\n",
    "feature = []\n",
    "profile_feature = ['svd_fea_'+str(i) for i in range(0, 33)]\n",
    "\n",
    "weather_feature = ['max_temp', 'min_temp', 'wind']\n",
    "\n",
    "# origin_num_feature = ['o_lng', 'o_lat', 'd_lng', 'd_lat', 'od_manhattan_distance', 'o_nearest_dis', 'd_nearest_dis'] + profile_feature # 2: 0.687966; 2.2: 0.687717\n",
    "\n",
    "# origin_num_feature = ['o_lng', 'o_lat', 'd_lng', 'd_lat', 'o_main_centroid_mean_dis',\n",
    "#                       'd_main_centroid_mean_dis', 'o_main_centroid_mode_dis', 'd_main_centroid_mode_dis',\n",
    "#                       'od_manhattan_distance', 'o_nearest_dis', 'd_nearest_dis', 'req_month'] + profile_feature  # 3: 0.6875\n",
    "\n",
    "# origin_num_feature = ['o_lng', 'o_lat', 'd_lng', 'd_lat', 'od_manhattan_distance', 'o_nearest_dis', 'd_nearest_dis','req_month'] + profile_feature # 4: 0 .687618\n",
    "\n",
    "\n",
    "# origin_num_feature = ['o_lng', 'o_lat', 'd_lng', 'd_lat', 'o_main_centroid_mode_dis', 'd_main_centroid_mode_dis',\n",
    "#                       'od_manhattan_distance', 'o_nearest_dis', 'd_nearest_dis', 'req_month'] + profile_feature # 5: 0.687256\n",
    "\n",
    "\n",
    "# origin_num_feature = ['o_lng', 'o_lat', 'd_lng', 'd_lat', 'o_main_centroid_mode_dis', 'd_main_centroid_mode_dis',\n",
    "#                       'od_manhattan_distance', 'o_nearest_dis', 'd_nearest_dis'] + profile_feature # 6: 0.68773\n",
    "origin_num_feature = ['o_lng', 'o_lat', 'd_lng', 'd_lat', 'o_main_centroid_mean_dis',\n",
    "                      'd_main_centroid_mean_dis', 'o_main_centroid_mode_dis', 'd_main_centroid_mode_dis',\n",
    "                      'od_manhattan_distance', 'o_nearest_dis', 'd_nearest_dis'] + profile_feature\n",
    "\n",
    "cate_feature = ['pid', 'max_dist_mode', 'min_dist_mode', 'max_price_mode',\n",
    "                'min_price_mode', 'max_eta_mode', 'min_eta_mode', 'first_mode', 'day_of_week', 'req_hour', 'weather']\n",
    "\n",
    "\n",
    "time_feature = ['if_holiday']\n",
    "feature = origin_num_feature + plan_features + \\\n",
    "    time_feature+cate_feature+weather_feature\n",
    "print('Got %s features' % str(len(feature)))\n",
    "feature=list(set(feature))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kYh6IcyBCvX1"
   },
   "source": [
    "# Data split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T10:10:47.452529Z",
     "start_time": "2019-05-11T10:10:44.010165Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "e_GiAJWUCvX1"
   },
   "outputs": [],
   "source": [
    "train_index = (data.req_time < '2018-11-23')\n",
    "train_x = data[train_index][feature].reset_index(drop=True)\n",
    "train_y = data[train_index].click_mode.reset_index(drop=True)\n",
    "\n",
    "valid_index = (data.req_time > '2018-11-23') & (data.req_time < '2018-12-01')\n",
    "valid_x = data[valid_index][feature].reset_index(drop=True)\n",
    "valid_y = data[valid_index].click_mode.reset_index(drop=True)\n",
    "\n",
    "test_index = (data.req_time > '2018-12-01')\n",
    "test_x = data[test_index][feature].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J7lCjcXYCvX3"
   },
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T10:10:49.723008Z",
     "start_time": "2019-05-11T10:10:49.717386Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "dmVtqDJHCvXl"
   },
   "outputs": [],
   "source": [
    "def f1_weighted(labels, preds):\n",
    "    preds = np.argmax(preds.reshape(12, -1), axis=0)\n",
    "    score = f1_score(y_true=labels, y_pred=preds, average='weighted')\n",
    "    return 'f1_weighted', score, True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T10:10:51.534636Z",
     "start_time": "2019-05-11T10:10:51.526685Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "IaN5aeUZCvX4"
   },
   "outputs": [],
   "source": [
    "lgb_model = lgb.LGBMClassifier(boosting_type=\"gbdt\",\n",
    "                               num_leaves=80,\n",
    "                               reg_alpha=10,\n",
    "                               reg_lambda=0.01,\n",
    "                               max_depth=6,\n",
    "                               n_estimators=2000,\n",
    "                               objective='multiclass',\n",
    "                               subsample=0.8,\n",
    "                               colsample_bytree=0.8,\n",
    "                               subsample_freq=1,\n",
    "                               min_child_samples=50,\n",
    "                               learning_rate=0.05,\n",
    "                               random_state=2019,\n",
    "                               metric=\"multiclass\",\n",
    "\n",
    "                               num_class=12,\n",
    "                               seed=2019,\n",
    "                               feature_fraction=0.8,\n",
    "                               bagging_fraction=0.8,\n",
    "                               bagging_freq=4,\n",
    "                               n_jobs=-1)\n",
    "# 2: 0.687966\n",
    "# 3: 0.687585\n",
    "# 4: 0.687659\n",
    "# 5: 0.687256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T10:12:38.772912Z",
     "start_time": "2019-05-11T10:10:53.352356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[10]\tvalid_0's multi_logloss: 1.4866\tvalid_0's f1_weighted: 0.622277\n",
      "[20]\tvalid_0's multi_logloss: 1.24532\tvalid_0's f1_weighted: 0.672804\n",
      "[30]\tvalid_0's multi_logloss: 1.11191\tvalid_0's f1_weighted: 0.681055\n",
      "[40]\tvalid_0's multi_logloss: 1.03145\tvalid_0's f1_weighted: 0.683266\n",
      "[50]\tvalid_0's multi_logloss: 0.980824\tvalid_0's f1_weighted: 0.684141\n",
      "[60]\tvalid_0's multi_logloss: 0.947456\tvalid_0's f1_weighted: 0.684807\n",
      "[70]\tvalid_0's multi_logloss: 0.925636\tvalid_0's f1_weighted: 0.685486\n",
      "[80]\tvalid_0's multi_logloss: 0.911059\tvalid_0's f1_weighted: 0.685529\n",
      "[90]\tvalid_0's multi_logloss: 0.899384\tvalid_0's f1_weighted: 0.685737\n",
      "[100]\tvalid_0's multi_logloss: 0.891007\tvalid_0's f1_weighted: 0.686032\n",
      "[110]\tvalid_0's multi_logloss: 0.884878\tvalid_0's f1_weighted: 0.686175\n",
      "[120]\tvalid_0's multi_logloss: 0.880202\tvalid_0's f1_weighted: 0.686317\n",
      "[130]\tvalid_0's multi_logloss: 0.876703\tvalid_0's f1_weighted: 0.686608\n",
      "[140]\tvalid_0's multi_logloss: 0.873837\tvalid_0's f1_weighted: 0.686495\n",
      "[150]\tvalid_0's multi_logloss: 0.871751\tvalid_0's f1_weighted: 0.686848\n",
      "[160]\tvalid_0's multi_logloss: 0.870142\tvalid_0's f1_weighted: 0.686846\n",
      "[170]\tvalid_0's multi_logloss: 0.868983\tvalid_0's f1_weighted: 0.686801\n",
      "[180]\tvalid_0's multi_logloss: 0.868224\tvalid_0's f1_weighted: 0.68671\n",
      "[190]\tvalid_0's multi_logloss: 0.867474\tvalid_0's f1_weighted: 0.68669\n",
      "[200]\tvalid_0's multi_logloss: 0.866889\tvalid_0's f1_weighted: 0.686601\n",
      "[210]\tvalid_0's multi_logloss: 0.866502\tvalid_0's f1_weighted: 0.686588\n",
      "[220]\tvalid_0's multi_logloss: 0.866241\tvalid_0's f1_weighted: 0.686473\n",
      "[230]\tvalid_0's multi_logloss: 0.86602\tvalid_0's f1_weighted: 0.686473\n",
      "[240]\tvalid_0's multi_logloss: 0.865792\tvalid_0's f1_weighted: 0.68652\n",
      "[250]\tvalid_0's multi_logloss: 0.865727\tvalid_0's f1_weighted: 0.686513\n",
      "Early stopping, best iteration is:\n",
      "[158]\tvalid_0's multi_logloss: 0.870447\tvalid_0's f1_weighted: 0.687042\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(bagging_fraction=0.8, bagging_freq=4, boosting_type='gbdt',\n",
       "        class_weight=None, colsample_bytree=0.8, feature_fraction=0.8,\n",
       "        importance_type='split', learning_rate=0.05, max_depth=6,\n",
       "        metric='multiclass', min_child_samples=50, min_child_weight=0.001,\n",
       "        min_split_gain=0.0, n_estimators=2000, n_jobs=-1, num_class=12,\n",
       "        num_leaves=80, objective='multiclass', random_state=2019,\n",
       "        reg_alpha=10, reg_lambda=0.01, seed=2019, silent=True,\n",
       "        subsample=0.8, subsample_for_bin=200000, subsample_freq=1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_set = [(valid_x, valid_y)]\n",
    "lgb_model.fit(train_x, train_y, eval_set=eval_set, eval_metric=f1_weighted,\n",
    "              categorical_feature=cate_feature, verbose=10, early_stopping_rounds=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sDIviFYVCvX6"
   },
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T10:14:48.955969Z",
     "start_time": "2019-05-11T10:14:48.905505Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "vXy7U40ECvX7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>pid</td>\n",
       "      <td>6297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>req_hour</td>\n",
       "      <td>2648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>first_mode</td>\n",
       "      <td>1514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>o_nearest_dis</td>\n",
       "      <td>1242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>std_eta</td>\n",
       "      <td>1074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>svd_mode_2</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>svd_mode_5</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>svd_fea_1</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>min_dist</td>\n",
       "      <td>814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>svd_mode_1</td>\n",
       "      <td>807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>svd_mode_0</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>svd_mode_9</td>\n",
       "      <td>797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>svd_mode_4</td>\n",
       "      <td>792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>d_nearest_dis</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>svd_mode_3</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>min_eta</td>\n",
       "      <td>647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>svd_mode_6</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>d_main_centroid_mean_dis</td>\n",
       "      <td>623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>svd_mode_8</td>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>o_main_centroid_mean_dis</td>\n",
       "      <td>602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>d_main_centroid_mode_dis</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>svd_mode_7</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>svd_fea_7</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>od_manhattan_distance</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>max_eta</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>std_dist</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>svd_fea_2</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>o_lng</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>o_lat</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>d_lng</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>svd_fea_18</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>svd_fea_24</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>min_price_mode</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>svd_fea_22</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>svd_fea_28</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>fea_3</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>svd_fea_26</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>wind</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>fea_7</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>svd_fea_27</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>svd_fea_30</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>svd_fea_25</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>fea_6</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>fea_10</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mode_feas_0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>fea_20</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>fea_21</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>fea_4</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>max_price_mode</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fea_18</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>fea_16</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fea_19</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>fea_17</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>if_holiday</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>min_price</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>fea_15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>fea_14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fea_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fea_11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>fea_9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     features  importance\n",
       "33                        pid        6297\n",
       "24                   req_hour        2648\n",
       "83                 first_mode        1514\n",
       "67              o_nearest_dis        1242\n",
       "57                    std_eta        1074\n",
       "25                 svd_mode_2        1056\n",
       "84                 svd_mode_5         894\n",
       "111                 svd_fea_1         832\n",
       "2                    min_dist         814\n",
       "7                  svd_mode_1         807\n",
       "75                 svd_mode_0         803\n",
       "87                 svd_mode_9         797\n",
       "85                 svd_mode_4         792\n",
       "66              d_nearest_dis         716\n",
       "5                  svd_mode_3         648\n",
       "100                   min_eta         647\n",
       "114                svd_mode_6         642\n",
       "30   d_main_centroid_mean_dis         623\n",
       "41                 svd_mode_8         613\n",
       "47   o_main_centroid_mean_dis         602\n",
       "23   d_main_centroid_mode_dis         583\n",
       "6                  svd_mode_7         581\n",
       "91                  svd_fea_7         571\n",
       "44      od_manhattan_distance         565\n",
       "12                    max_eta         564\n",
       "94                   std_dist         526\n",
       "72                  svd_fea_2         521\n",
       "104                     o_lng         478\n",
       "110                     o_lat         476\n",
       "56                      d_lng         463\n",
       "..                        ...         ...\n",
       "10                 svd_fea_18         124\n",
       "74                 svd_fea_24         122\n",
       "28             min_price_mode         121\n",
       "93                 svd_fea_22         117\n",
       "106                svd_fea_28         105\n",
       "62                      fea_3         104\n",
       "32                 svd_fea_26         101\n",
       "27                       wind         100\n",
       "59                      fea_7          97\n",
       "105                svd_fea_27          91\n",
       "101                svd_fea_30          91\n",
       "98                 svd_fea_25          86\n",
       "113                     fea_6          82\n",
       "112                    fea_10          73\n",
       "1                 mode_feas_0          55\n",
       "48                     fea_20          47\n",
       "109                    fea_21          46\n",
       "103                     fea_4          43\n",
       "49             max_price_mode          38\n",
       "17                     fea_18          35\n",
       "60                     fea_16          35\n",
       "21                     fea_19          32\n",
       "38                     fea_17          31\n",
       "18                 if_holiday          16\n",
       "31                  min_price          11\n",
       "69                     fea_15           5\n",
       "107                    fea_14           1\n",
       "4                       fea_5           0\n",
       "14                     fea_11           0\n",
       "54                      fea_9           0\n",
       "\n",
       "[115 rows x 2 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = pd.DataFrame()\n",
    "imp['features'] = feature\n",
    "imp['importance'] = lgb_model.feature_importances_\n",
    "imp = imp.sort_values('importance', ascending=False)\n",
    "imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-06T16:30:22.470029Z",
     "start_time": "2019-05-06T16:30:17.850529Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x7200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 100))\n",
    "sns.barplot(x=\"importance\",\n",
    "            y=\"features\",\n",
    "            data=imp)\n",
    "plt.title('LightGBM Features (avg over folds)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8UAV2I0HCvX8"
   },
   "source": [
    "# Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T08:04:29.090923Z",
     "start_time": "2019-05-11T08:04:24.727946Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "6FQLgnBzCvX9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.686892048916592\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_id</th>\n",
       "      <th>counts*f1_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.087414</td>\n",
       "      <td>0.346894</td>\n",
       "      <td>0.877013</td>\n",
       "      <td>0.216206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.144617</td>\n",
       "      <td>0.682704</td>\n",
       "      <td>0.609744</td>\n",
       "      <td>0.775499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.313324</td>\n",
       "      <td>0.901433</td>\n",
       "      <td>0.851168</td>\n",
       "      <td>0.958008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.044598</td>\n",
       "      <td>0.092504</td>\n",
       "      <td>0.470779</td>\n",
       "      <td>0.051291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.024453</td>\n",
       "      <td>0.016508</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.008387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.097574</td>\n",
       "      <td>0.841299</td>\n",
       "      <td>0.772518</td>\n",
       "      <td>0.923525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.019893</td>\n",
       "      <td>0.143583</td>\n",
       "      <td>0.361022</td>\n",
       "      <td>0.089611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.177920</td>\n",
       "      <td>0.788144</td>\n",
       "      <td>0.704236</td>\n",
       "      <td>0.894751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.004559</td>\n",
       "      <td>0.233607</td>\n",
       "      <td>0.286432</td>\n",
       "      <td>0.197232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.049931</td>\n",
       "      <td>0.510987</td>\n",
       "      <td>0.570261</td>\n",
       "      <td>0.462875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.028475</td>\n",
       "      <td>0.555870</td>\n",
       "      <td>0.483792</td>\n",
       "      <td>0.653186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.007241</td>\n",
       "      <td>0.454139</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.442266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    class_id  counts*f1_score  f1_score  precision    recall\n",
       "0        0.0         0.087414  0.346894   0.877013  0.216206\n",
       "1        1.0         0.144617  0.682704   0.609744  0.775499\n",
       "2        2.0         0.313324  0.901433   0.851168  0.958008\n",
       "3        3.0         0.044598  0.092504   0.470779  0.051291\n",
       "4        4.0         0.024453  0.016508   0.520000  0.008387\n",
       "5        5.0         0.097574  0.841299   0.772518  0.923525\n",
       "6        6.0         0.019893  0.143583   0.361022  0.089611\n",
       "7        7.0         0.177920  0.788144   0.704236  0.894751\n",
       "8        8.0         0.004559  0.233607   0.286432  0.197232\n",
       "9        9.0         0.049931  0.510987   0.570261  0.462875\n",
       "10      10.0         0.028475  0.555870   0.483792  0.653186\n",
       "11      11.0         0.007241  0.454139   0.466667  0.442266"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = lgb_model.predict(valid_x)\n",
    "df_analysis = pd.DataFrame()\n",
    "df_analysis['sid'] = data[valid_index]['sid']\n",
    "df_analysis['label'] = valid_y.values\n",
    "df_analysis['pred'] = pred\n",
    "df_analysis['label'] = df_analysis['label'].astype(int)\n",
    "dic_ = df_analysis['label'].value_counts(normalize=True)\n",
    "\n",
    "score_df = pd.DataFrame(\n",
    "    columns=['class_id', 'counts*f1_score', 'f1_score', 'precision', 'recall'])\n",
    "\n",
    "\n",
    "def get_weighted_fscore(y_pred, y_true):\n",
    "    f_score = 0\n",
    "    for i in range(12):\n",
    "        yt = y_true == i\n",
    "        yp = y_pred == i\n",
    "        f_score += dic_[i] * f1_score(y_true=yt, y_pred=yp)\n",
    "        score_df.loc[i] = [i, dic_[i], f1_score(y_true=yt, y_pred=yp), precision_score(\n",
    "            y_true=yt, y_pred=yp), recall_score(y_true=yt, y_pred=yp)]\n",
    "    print(f_score)\n",
    "    return score_df\n",
    "\n",
    "\n",
    "score_df = get_weighted_fscore(\n",
    "    y_true=df_analysis['label'], y_pred=df_analysis['pred'])\n",
    "score_df\n",
    "\n",
    "# 0.7095882458513125\n",
    "# 0.6881122981481186\n",
    "# 0.6876586015532968\n",
    "# 0.6877299824446942"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T08:06:28.196804Z",
     "start_time": "2019-05-11T08:05:02.438479Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "xVhOpSZeCvX_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172\n",
      "fit over\n",
      "94358\n",
      "2     34094\n",
      "7     22415\n",
      "1     17977\n",
      "5     10874\n",
      "10     3497\n",
      "0      2182\n",
      "9      1882\n",
      "11      453\n",
      "3       366\n",
      "6       313\n",
      "8       260\n",
      "4        45\n",
      "Name: recommend_mode, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "all_train_x = data[data.req_time <\n",
    "                   '2018-12-01'][feature].reset_index(drop=True)\n",
    "all_train_y = data[data.req_time <\n",
    "                   '2018-12-01'].click_mode.reset_index(drop=True)\n",
    "print(lgb_model.best_iteration_)\n",
    "lgb_model.n_estimators = lgb_model.best_iteration_\n",
    "lgb_model.fit(all_train_x, all_train_y, categorical_feature=cate_feature)\n",
    "print('fit over')\n",
    "result = pd.DataFrame()\n",
    "result['sid'] = data[test_index]['sid']\n",
    "result['recommend_mode'] = lgb_model.predict(test_x)\n",
    "result['recommend_mode'] = result['recommend_mode'].astype(int)\n",
    "print(len(result))\n",
    "print(result['recommend_mode'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T05:32:22.455263Z",
     "start_time": "2019-05-11T05:32:22.189432Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "XfZbGMgHCvYC"
   },
   "outputs": [],
   "source": [
    "result[['sid', 'recommend_mode']].to_csv(\n",
    "    project_path + 'Data/submit.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save train & test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T09:36:13.774110Z",
     "start_time": "2019-05-11T09:36:13.767589Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['svd_fea_0',\n",
       " 'mode_feas_0',\n",
       " 'min_dist',\n",
       " 'fea_0',\n",
       " 'fea_5',\n",
       " 'svd_mode_3',\n",
       " 'svd_mode_7',\n",
       " 'svd_mode_1',\n",
       " 'svd_fea_13',\n",
       " 'svd_fea_12',\n",
       " 'svd_fea_18',\n",
       " 'mode_feas_1',\n",
       " 'max_eta',\n",
       " 'max_dist_mode',\n",
       " 'fea_11',\n",
       " 'mode_feas_8',\n",
       " 'mean_price',\n",
       " 'fea_18',\n",
       " 'if_holiday',\n",
       " 'min_eta_mode',\n",
       " 'svd_fea_11',\n",
       " 'fea_19',\n",
       " 'max_temp',\n",
       " 'd_main_centroid_mode_dis',\n",
       " 'req_hour',\n",
       " 'svd_mode_2',\n",
       " 'mode_feas_5',\n",
       " 'wind',\n",
       " 'min_price_mode',\n",
       " 'weather',\n",
       " 'd_main_centroid_mean_dis',\n",
       " 'min_price',\n",
       " 'svd_fea_26',\n",
       " 'pid',\n",
       " 'mode_feas_2',\n",
       " 'min_dist_mode',\n",
       " 'svd_fea_5',\n",
       " 'svd_fea_17',\n",
       " 'fea_17',\n",
       " 'svd_fea_21',\n",
       " 'max_price',\n",
       " 'svd_mode_8',\n",
       " 'min_temp',\n",
       " 'std_price',\n",
       " 'od_manhattan_distance',\n",
       " 'mode_feas_9',\n",
       " 'svd_fea_29',\n",
       " 'o_main_centroid_mean_dis',\n",
       " 'fea_20',\n",
       " 'max_price_mode',\n",
       " 'svd_fea_15',\n",
       " 'svd_fea_6',\n",
       " 'svd_fea_19',\n",
       " 'fea_2',\n",
       " 'fea_9',\n",
       " 'svd_fea_8',\n",
       " 'd_lng',\n",
       " 'std_eta',\n",
       " 'svd_fea_9',\n",
       " 'fea_7',\n",
       " 'fea_16',\n",
       " 'day_of_week',\n",
       " 'fea_3',\n",
       " 'mean_dist',\n",
       " 'd_lat',\n",
       " 'fea_8',\n",
       " 'd_nearest_dis',\n",
       " 'o_nearest_dis',\n",
       " 'svd_fea_23',\n",
       " 'fea_15',\n",
       " 'mode_feas_3',\n",
       " 'max_dist',\n",
       " 'svd_fea_2',\n",
       " 'mode_feas_6',\n",
       " 'svd_fea_24',\n",
       " 'svd_mode_0',\n",
       " 'max_eta_mode',\n",
       " 'fea_12',\n",
       " 'mode_feas_11',\n",
       " 'svd_fea_16',\n",
       " 'mode_feas_10',\n",
       " 'mode_feas_7',\n",
       " 'o_main_centroid_mode_dis',\n",
       " 'first_mode',\n",
       " 'svd_mode_5',\n",
       " 'svd_mode_4',\n",
       " 'svd_fea_14',\n",
       " 'svd_mode_9',\n",
       " 'svd_fea_20',\n",
       " 'svd_fea_10',\n",
       " 'fea_1',\n",
       " 'svd_fea_7',\n",
       " 'svd_fea_4',\n",
       " 'svd_fea_22',\n",
       " 'std_dist',\n",
       " 'svd_fea_32',\n",
       " 'svd_fea_3',\n",
       " 'mean_eta',\n",
       " 'svd_fea_25',\n",
       " 'svd_fea_31',\n",
       " 'min_eta',\n",
       " 'svd_fea_30',\n",
       " 'fea_13',\n",
       " 'fea_4',\n",
       " 'o_lng',\n",
       " 'svd_fea_27',\n",
       " 'svd_fea_28',\n",
       " 'fea_14',\n",
       " 'mode_feas_4',\n",
       " 'fea_21',\n",
       " 'o_lat',\n",
       " 'svd_fea_1',\n",
       " 'fea_10',\n",
       " 'fea_6',\n",
       " 'svd_mode_6',\n",
       " 'click_mode']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_s = feature+['click_mode']\n",
    "feature_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T09:37:40.869787Z",
     "start_time": "2019-05-11T09:36:28.520787Z"
    }
   },
   "outputs": [],
   "source": [
    "train_index_s = (data.req_time < '2018-12-01')\n",
    "train_s = data[train_index_s][feature_s].reset_index(drop=True)\n",
    "\n",
    "test_index_s = (data.req_time > '2018-12-01')\n",
    "test_s = data[test_index_s][feature].reset_index(drop=True)\n",
    "\n",
    "train_s.to_csv(project_path+r'Data/train.csv', index=False)\n",
    "test_s.to_csv(project_path+r'Data/test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-11T09:37:54.827294Z",
     "start_time": "2019-05-11T09:37:43.152709Z"
    }
   },
   "outputs": [],
   "source": [
    "tr=pd.read_csv(project_path+r'Data/train.csv')\n",
    "ts=pd.read_csv(project_path+r'Data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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